Assessing processes in uncertain, complex physical phenomena and manufacturing
Bibliographic record
Abstract
PREDICT (Performance and Reliability Evaluation with Diverse Information Combination and Tracking) is a set of structured quantitative approaches for the evaluation of system performance based on multiple information sources. The methodology integrates diverse types and sources of information, and their associated uncertainties, to develop full distributions for performance metrics, such as reliability. The successful application of PREDICT has involved system performance assessment in automotive product development, aging nuclear weapons, and fatigued turbine jet engines. In each of these applications, complex physical, mechanical and materials processes affect performance, safety and reliability assessments. Processes also include the physical actions taken during manufacturing, quality control, inspections, assembly, etc. and the steps involved in product design, development and certification. In this paper, we will examine the various types of processes involved in the decision making leading to production in an automotive system reliability example. Analysis of these processes includes not only understanding their impact on performance and reliability, but also the uncertainties associated with them. The automotive example demonstrates some of the tools used in tackling the complex problem of understanding processes. While some tools and methods exist for understanding processes (man made and natural) and the uncertainties associated with them, many of the complex issues discussed are open for continued research efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".